



import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a      :  Tuple			     =			logging.get_logger(__name__)

enable_full_determinism()







class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	UNetaDModel
			A      		=	'''sample'''

			@property
			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		int							      =     4
						UpperCAmelCase_:		Optional[Any]							      =     3
						UpperCAmelCase_:		Dict							      =     (32, 32)

						UpperCAmelCase_:		List[str]							      =     floats_tensor((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     torch.tensor([10]					).to(SCREAMING_SNAKE_CASE_					)

						return {"sample": noise, "timestep": time_step}

			@property
			def      __snake_case       (self					)       ->   Dict:
						return (3, 32, 32)

			@property
			def      __snake_case       (self					)       ->   List[Any]:
						return (3, 32, 32)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		List[str]							      =     {
						    """block_out_channels""": (32, 64),
						    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
						    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
						    """attention_head_dim""": 3,
						    """out_channels""": 3,
						    """in_channels""": 3,
						    """layers_per_block""": 2,
						    """sample_size""": 32,
						}
						UpperCAmelCase_:		Dict							      =     self.dummy_input
						return init_dict, inputs_dict







class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	UNetaDModel
			A      		=	'''sample'''

			@property
			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		Union[str, Any]							      =     4
						UpperCAmelCase_:		Union[str, Any]							      =     4
						UpperCAmelCase_:		Any							      =     (32, 32)

						UpperCAmelCase_:		Optional[int]							      =     floats_tensor((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     torch.tensor([10]					).to(SCREAMING_SNAKE_CASE_					)

						return {"sample": noise, "timestep": time_step}

			@property
			def      __snake_case       (self					)       ->   Optional[Any]:
						return (4, 32, 32)

			@property
			def      __snake_case       (self					)       ->   int:
						return (4, 32, 32)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		int							      =     {
						    """sample_size""": 32,
						    """in_channels""": 4,
						    """out_channels""": 4,
						    """layers_per_block""": 2,
						    """block_out_channels""": (32, 64),
						    """attention_head_dim""": 32,
						    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
						    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
						}
						UpperCAmelCase_:		List[str]							      =     self.dummy_input
						return init_dict, inputs_dict

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""",   output_loading_info=SCREAMING_SNAKE_CASE_					)

						self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)
						self.assertEqual(len(loading_info["""missing_keys"""]					),   0					)

						model.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     model(**self.dummy_input					).sample

						assert image is not None, "Make sure output is not None"

			@unittest.skipIf(torch_device != """cuda""",   """This test is supposed to run on GPU"""					)
			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""",   output_loading_info=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     model(**self.dummy_input					).sample

						assert image is not None, "Make sure output is not None"

			@unittest.skipIf(torch_device != """cuda""",   """This test is supposed to run on GPU"""					)
			def      __snake_case       (self					)       ->   List[str]:
						# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""",   output_loading_info=SCREAMING_SNAKE_CASE_					)
						model_accelerate.to(SCREAMING_SNAKE_CASE_					)
						model_accelerate.eval()

						UpperCAmelCase_:		Optional[Any]							      =     torch.randn(
						    1,   model_accelerate.config.in_channels,   model_accelerate.config.sample_size,   model_accelerate.config.sample_size,   generator=torch.manual_seed(0					),   )
						UpperCAmelCase_:		Optional[Any]							      =     noise.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     torch.tensor([10] * noise.shape[0]					).to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     model_accelerate(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)["""sample"""]

						# two models don't need to stay in the device at the same time
						del model_accelerate
						torch.cuda.empty_cache()
						gc.collect()

						UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     UNetaDModel.from_pretrained(
						    """fusing/unet-ldm-dummy-update""",   output_loading_info=SCREAMING_SNAKE_CASE_,   low_cpu_mem_usage=SCREAMING_SNAKE_CASE_					)
						model_normal_load.to(SCREAMING_SNAKE_CASE_					)
						model_normal_load.eval()
						UpperCAmelCase_:		Any							      =     model_normal_load(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)["""sample"""]

						assert torch_all_close(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   rtol=1E-3					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""					)
						model.eval()
						model.to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[str]							      =     torch.randn(
						    1,   model.config.in_channels,   model.config.sample_size,   model.config.sample_size,   generator=torch.manual_seed(0					),   )
						UpperCAmelCase_:		Optional[Any]							      =     noise.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor([10] * noise.shape[0]					).to(SCREAMING_SNAKE_CASE_					)

						with torch.no_grad():
									UpperCAmelCase_:		Optional[int]							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					).sample

						UpperCAmelCase_:		Tuple							      =     output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						UpperCAmelCase_:		Dict							      =     torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]					)
						# fmt: on

						self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   rtol=1E-3					)					)







class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	UNetaDModel
			A      		=	'''sample'''

			@property
			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_=(32, 32)					)       ->   List[str]:
						UpperCAmelCase_:		Union[str, Any]							      =     4
						UpperCAmelCase_:		str							      =     3

						UpperCAmelCase_:		List[str]							      =     floats_tensor((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor(batch_size * [10]					).to(dtype=torch.intaa,   device=SCREAMING_SNAKE_CASE_					)

						return {"sample": noise, "timestep": time_step}

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						return (3, 32, 32)

			@property
			def      __snake_case       (self					)       ->   List[Any]:
						return (3, 32, 32)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		List[Any]							      =     {
						    """block_out_channels""": [32, 64, 64, 64],
						    """in_channels""": 3,
						    """layers_per_block""": 1,
						    """out_channels""": 3,
						    """time_embedding_type""": """fourier""",
						    """norm_eps""": 1E-6,
						    """mid_block_scale_factor""": math.sqrt(2.0					),
						    """norm_num_groups""": None,
						    """down_block_types""": [
						        """SkipDownBlock2D""",
						        """AttnSkipDownBlock2D""",
						        """SkipDownBlock2D""",
						        """SkipDownBlock2D""",
						    ],
						    """up_block_types""": [
						        """SkipUpBlock2D""",
						        """SkipUpBlock2D""",
						        """AttnSkipUpBlock2D""",
						        """SkipUpBlock2D""",
						    ],
						}
						UpperCAmelCase_:		Tuple							      =     self.dummy_input
						return init_dict, inputs_dict

			@slow
			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256""",   output_loading_info=SCREAMING_SNAKE_CASE_					)
						self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)
						self.assertEqual(len(loading_info["""missing_keys"""]					),   0					)

						model.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     self.dummy_input
						UpperCAmelCase_:		Optional[int]							      =     floats_tensor((4, 3) + (256, 256)					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     noise
						UpperCAmelCase_:		int							      =     model(**SCREAMING_SNAKE_CASE_					)

						assert image is not None, "Make sure output is not None"

			@slow
			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Any							      =     UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""					)
						model.to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     4
						UpperCAmelCase_:		Optional[int]							      =     3
						UpperCAmelCase_:		Tuple							      =     (256, 256)

						UpperCAmelCase_:		Union[str, Any]							      =     torch.ones((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     torch.tensor(batch_size * [1E-4]					).to(SCREAMING_SNAKE_CASE_					)

						with torch.no_grad():
									UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					).sample

						UpperCAmelCase_:		Any							      =     output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]					)
						# fmt: on

						self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   rtol=1E-2					)					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Optional[int]							      =     UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""					)
						model.to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     4
						UpperCAmelCase_:		Any							      =     3
						UpperCAmelCase_:		Optional[Any]							      =     (32, 32)

						UpperCAmelCase_:		List[str]							      =     torch.ones((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     torch.tensor(batch_size * [1E-4]					).to(SCREAMING_SNAKE_CASE_					)

						with torch.no_grad():
									UpperCAmelCase_:		Any							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					).sample

						UpperCAmelCase_:		List[Any]							      =     output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]					)
						# fmt: on

						self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   rtol=1E-2					)					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						# not required for this model
						pass











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''timm_backbone'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     backbone
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		Optional[Any]							      =     features_only
						UpperCAmelCase_:		Any							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     out_indices if out_indices is not None else (-1,)






